Ever since we started our research and development activities in 1996, our consistent use of smart technologies has enabled us to carry out R&D work faster, more precisely and more efficiently. To further expand our leading market position and keep up with the complexity of our research fields, we are continuously refining our digital research and development methods. The use of innovative VR, XR and AI technologies not only allows us to reduce the processing time and error rates of scientific calculations and analyse large amounts of data, but also to test new conceptual approaches to extremely complex problems. Our n-AI algorithms have an increasing impact on the organisation of our R&D activities and the efficiency of our research processes.
One of the benefits of the nanoFlowcell AI system – or n-AI for short – is that it improves productivity in the development of materials. For example, we use n-AI technology to speed up the synthesis and testing phases when we develop new materials, as well as to gain faster insights into how physical and chemical structures affect the properties of a molecule. As a result, we are able to accelerate the discovery and development of breakthrough technologies, such as membrane structures for our nanoFlowcell® or powerful new molecules for electrolyte solutions. Indeed, nanoFlowcell® and bi-ION® are prime examples of our uncompromising approach to research and development.
It is only during our intensive work on applications that we realise their potential for improvement: When we started thinking about the power system in electric vehicles and how to adapt a nanoFlowcell® to the conventional high-voltage system used in them, our engineers ultimately broke with the technological mainstream and instead developed the world’s first road-legal low-voltage electric drive system. Our nanoFlowcell® 48VOLT low-voltage electric drive system delivers uncompromising improvements over comparable high-voltage systems in terms of safety, weight, cost and performance.
The situation is similar in other areas of our work. In recent years, we have made great strides in the fields of AI and machine learning, especially since we decided to no longer use our AI applications exclusively for our internal R&D processes, but also to develop applications in robotics, intelligent production automation and energy distribution systems or improved autonomous drive systems. And we have now reached a stage where n-AI is not only being used in the application development process, but is being built into the applications themselves. Our aim is to make applications smarter and thus support a sustainable and desirable triad of economy, society and nature.